This paper explores and compares various data augmentation techniques in image classification, focusing on their effectiveness and potential improvements. The authors use a small subset of the ImageNet dataset to evaluate methods such as traditional transformations (cropping, rotating, flipping), GANs for style transfer, and neural augmentation, where a neural network learns to generate augmentations that improve classification performance. The experiments are conducted on two datasets: tiny-imagenet-200 (dogs vs. cats and dogs vs. goldfish) and MNIST (0s vs. 8s). The results show that neural augmentation outperforms traditional methods in some cases, particularly in the dogs vs. goldfish classification task, achieving 91.5% accuracy compared to 85.5% without augmentation. However, neural augmentation does not provide significant benefits for the MNIST dataset, likely due to the simplicity of the digits and the effectiveness of existing CNNs. The authors conclude that combining different augmentation techniques, such as traditional methods followed by neural augmentation, could be a promising approach. Future work includes exploring more complex architectures, larger datasets, and applying these techniques to video data for improved safety in self-driving vehicles.This paper explores and compares various data augmentation techniques in image classification, focusing on their effectiveness and potential improvements. The authors use a small subset of the ImageNet dataset to evaluate methods such as traditional transformations (cropping, rotating, flipping), GANs for style transfer, and neural augmentation, where a neural network learns to generate augmentations that improve classification performance. The experiments are conducted on two datasets: tiny-imagenet-200 (dogs vs. cats and dogs vs. goldfish) and MNIST (0s vs. 8s). The results show that neural augmentation outperforms traditional methods in some cases, particularly in the dogs vs. goldfish classification task, achieving 91.5% accuracy compared to 85.5% without augmentation. However, neural augmentation does not provide significant benefits for the MNIST dataset, likely due to the simplicity of the digits and the effectiveness of existing CNNs. The authors conclude that combining different augmentation techniques, such as traditional methods followed by neural augmentation, could be a promising approach. Future work includes exploring more complex architectures, larger datasets, and applying these techniques to video data for improved safety in self-driving vehicles.